{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 用户画像A卡0-0.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 数据准备\n",
    "- 读取导出的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(r\"d:\\qian\\data\\user_profile\\up_0-04.txt\",sep='\\t',\n",
    "                  dtype={\"uid\":str,\"user_name\":str,\"gender\":int,\"birthday\":str,\"age\":int,\"work_city\":str,\"province\":str,\"flight_power\":float,\"flight_cabin_trend\":str,\"hotel_power\":float,\"hotel_area_trend\":str,\"hotel_band_trend\":str,\"train_power\":float,\"train_cabin_trend\":str,\"credit_max_amount\":float,\"holiday_avg_count\":int,\"holiday_power\":float,\"holiday_city_tendency\":str})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>uid</th>\n",
       "      <th>active_date</th>\n",
       "      <th>rate_before</th>\n",
       "      <th>rate_after</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00000168</td>\n",
       "      <td>2016-12-15</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00000442</td>\n",
       "      <td>2017-09-19</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00001449</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>0.205128</td>\n",
       "      <td>0.346154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00004066</td>\n",
       "      <td>2017-11-05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00005695</td>\n",
       "      <td>2017-03-12</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        uid active_date  rate_before  rate_after\n",
       "0  00000168  2016-12-15     0.000000    0.000000\n",
       "1  00000442  2017-09-19     0.000000    0.800000\n",
       "2  00001449  2017-06-01     0.205128    0.346154\n",
       "3  00004066  2017-11-05     1.000000    0.000000\n",
       "4  00005695  2017-03-12     0.000000    0.500000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.set_palette('bright', desat=.6)\n",
    "sns.set_context(rc={'figure.figsize': (10, 5) } )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 数据按BU分开，排除激活时间在最近三个月的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x143e6780>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QhT7zaevY8sxBbvjngzQm4cz6qo4asn6gxoufs56xow3++ztH+b/hhxg7OsmRsQbDIxPc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf1dfc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "sns.distplot(df['rate_before'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 显示分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb09d048>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iPJcA55jZX9UfdzHb2vlem3MSOS4KcomrSQAz20XUQrmZqHK+hGgvnmZp4MPu\n/qH6r9lItNf5vOo7MN5BdGOLrwGHiCr5xaSBV7n7kfo5zgAeJ6rsJxf7hSLLpdaKxN0O4H+5++eI\n+tObiUK1wmyhcjvwFjPrr+9hfwvRh5YLOR846O7XEFXTr6mfs1Xre7wDwMyeB9wH9C5jXiJtU5BL\n3P01cKOZ3Q38V+Au4CyiXeQ2mtmN7v5/gS8QhfL9RG2STy9yzm8AKTP7MbAf+Fn9nK3+BXivmf0W\n8KfA9vqHrP8IvMXdx1ZgfiJL0qoVEZGYU49cEsnMeoDvLnD4fe7+pbUcj8hyqCIXEYk59chFRGJO\nQS4iEnMKchGRmFOQi4jEnIJcRCTm/j8p0rk2JTGKqAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd7da9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['rate_after'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 可以看到开通后的数字要高一些但都有一个长尾，不容易观察，下图是取10的对数的结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ifju/k9ZoM/f+y0a6jzqM/voYK09ZzJn3vci1Lx5B4vTzuSX3DHNzcU60d5I7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf3a8be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "df['log_before'] = df['rate_before'].apply(lambda x: math.log(x) if x != 0 else 0)\n",
    "df['log_after'] = df['rate_after'].apply(lambda x: math.log(x) if x != 0 else 0)\n",
    "ax1=sns.distplot(df['log_before'],color='r')\n",
    "ax2=sns.distplot(df['log_after'],color='g')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "##### 依然不是很清晰，去除开通前后都是0的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = df[(df.rate_before !=0) | (df.rate_after !=0) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ifju/k9ZoM/f+y0a6jzqM/voYK09ZzJn3vci1Lx5B4vTzuSX3DHNzcU60d5I7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf858cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1=sns.distplot(df['log_before'],color='r')\n",
    "ax2=sns.distplot(df['log_after'],color='g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from numpy import mean, median\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.43480325154001453"
      ]
     },
     "execution_count": 50,
     "metadata": {},
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   "cell_type": "code",
   "execution_count": 51,
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   },
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      ]
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     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
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   "cell_type": "code",
   "execution_count": 54,
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    "collapsed": false
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   "outputs": [
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     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
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   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
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   "outputs": [
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      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
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